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Strong brand, metro location, mid-level generalist data role, and broad tech stack increase competition.
Core data engineering skills and common tooling are widely transferable across industries.
Explicit 5+ years requirement plus mandatory SQL, Python, Snowflake, Airflow, and Kafka raises strictness.
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Build and maintain data pipelines and models to ingest, cleanse, deduplicate, normalize, and prepare enterprise and platform data for data products.
Develop ELT/ETL processes, data warehousing solutions (e.g., Snowflake), and reporting structures for analytics supporting internal stakeholders across multiple departments.
Ensure data quality, governance, and compliance while collaborating effectively with technical and non-technical teams to deliver timely and accurate data insights.
Bachelor's degree in Computer Science, Engineering, or related field.
5+ years of experience in data engineering including data pipelines, data modeling, and data architecture.
5+ years of advanced SQL experience; 3+ years of Python experience for data manipulation and pipeline development.
Proficiency with ETL/ELT processes, platforms (e.g., Airflow), data warehousing (e.g., Snowflake), and experience in data governance and quality assurance.
Experienced in enterprise data platforms with understanding of integrating enterprise systems like CRM, ERP, Marketing Automation, Financial Systems (preferred).
Capable of translating complex data requirements into scalable pipeline solutions and data models to support cross-functional business insights.
Effective communicator who can engage non-technical stakeholders and promote data governance and quality across diverse teams.